import os
import base64
file_path = os.path.dirname(__file__)
download_path = os.path.join(file_path, 'download')
data_path = os.path.join(file_path, 'data')
list_path = os.path.join(file_path, 'list')
img_path = os.path.join(file_path, 'imgs')
import csv
import numpy as np
import time

if 'download' not in os.listdir(file_path):
    os.makedirs(download_path)
if 'data' not in os.listdir(file_path):
    os.makedirs(data_path)
if 'list' not in os.listdir(file_path):
    os.makedirs(list_path)
if 'imgs' not in os.listdir(file_path):
    os.makedirs(img_path)
    img1 = 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"
    with open(os.path.join(img_path, 'Logo.ico'), 'wb') as f:
        f.write(base64.b64decode(img1))
    with open(os.path.join(img_path, 'super.png'), 'wb') as f:
        f.write(base64.b64decode(img2))
    with open(os.path.join(img_path, 'title.png'), 'wb') as f:
        f.write(base64.b64decode(img3))


def visual_timestamp():
    a = time.mktime(time.localtime())
    return str(int(a))


# load sequence data set
def load_data(file, sql='MBPs.csv', out='new_data'):
    with open(file, 'r') as u:
        lines = u.readlines()
    result = ''
    for i in lines:
        i = i.strip('\n')
        if i and i[0] == '>':
            result = result + '\n' + i + '\n'
        else:
            result = result + i
    result = result[1:].split('\n')
    sq_dic = {}
    for i in range(len(result)-1):
        if '>' in result[i]:
            sq_dic[result[i]] = result[i+1]
    if out not in os.listdir(data_path):
        os.makedirs(os.path.join(data_path, out))
    t = 0
    path = []
    for key in sq_dic:
        t += 1
        content = key + '\n' + sq_dic[key]
        save_path = os.path.join(os.path.join(data_path, out), str(t))
        index = key.split('-')[0][1:] + ',' + '-'.join(key.split('-')[1:]) + ',' + out + ',' + str(t)
        path.append(key.split('-')[0][1:] + '\t' + out + '\t' + str(t) + '\t' + '-'.join(key.split('-')[1:]))
        with open(save_path, 'w') as f:
            f.write(content)
        with open(os.path.join(list_path, sql), 'a') as f:
            f.write(index + '\n')
    return path


# def search_data(command):
#     if ',' in command:
#         line = command.split(',')
#     elif ' ' in command:
#         line = command.split(' ')
#     else:
#         line = [command]
#     with open(os.path.join(list_path, 'MBPs.csv'), 'r') as f:
#         reader = csv.reader(f)
#         result = np.array(list(reader))
#     list_dic = []
#     for i in range(len(line)):
#         list_dic.append({})
#     for i in range(len(line)):
#         word = line[i]
#         for j in range(len(result)):
#             if word == result[j, 0]:
#                 list_dic[i][result[j, 0] + '\t' + result[j, 2] + '\t' + result[j, 3] + '\t' + result[j, 1]] = 0
#             elif word == result[j, 2]:
#                 list_dic[i][result[j, 0] + '\t' + result[j, 2] + '\t' + result[j, 3] + '\t' + result[j, 1]] = 0
#             elif word == result[j, 3]:
#                 list_dic[i][result[j, 0] + '\t' + result[j, 2] + '\t' + result[j, 3] + '\t' + result[j, 1]] = 0
#     out_dic = list_dic[0]
#     for i in list_dic:
#         out_dic = dict(out_dic.items() & i.items())
#     out = []
#     for key in out_dic:
#         out.append(key)
#     return out


def open_data(list_name):
    with open(os.path.join(list_path, list_name), 'r') as f:
        reader = csv.reader(f)
        result = list(reader)
    out = []
    for line in result:
        out.append('\t'.join([line[0], line[2], line[3], line[1]]))
    return out


def search_data(command, search_list):
    if ',' in command:
        line = command.split(',')
    elif ' ' in command:
        line = command.split(' ')
    else:
        line = [command]
    for i in range(len(search_list)):
        search_list[i] = search_list[i].split('\t')
    result = np.array(search_list)
    list_dic = []
    for i in range(len(line)):
        list_dic.append({})
    for i in range(len(line)):
        word = line[i]
        for j in range(len(result)):
            if word == result[j, 0]:
                list_dic[i][result[j, 0] + '\t' + result[j, 1] + '\t' + result[j, 2] + '\t' + result[j, 3]] = 0
            elif word == result[j, 1]:
                list_dic[i][result[j, 0] + '\t' + result[j, 1] + '\t' + result[j, 2] + '\t' + result[j, 3]] = 0
            elif word == result[j, 2]:
                list_dic[i][result[j, 0] + '\t' + result[j, 1] + '\t' + result[j, 2] + '\t' + result[j, 3]] = 0
    out_dic = list_dic[0]
    for i in list_dic:
        out_dic = dict(out_dic.items() & i.items())
    out = []
    for key in out_dic:
        out.append(key)
    return out


def standard_file(file):
    with open(file, 'r') as u:
        lines = u.readlines()
    result = ''
    for i in lines:
        i = i.strip('\n')
        if i and i[0] == '>':
            result = result + '\n' + i + '\n'
        else:
            result = result + i
    result = result[1:].split('\n')
    out = ''
    for line in result:
        if '>' in line:
            if '-' not in line:
                out += '\n' + line.split(':')[0].split(' ')[0].split('|')[0].lower() + '-NA' + '\n'
            else:
                out += '\n' + line.split('-')[0].split(':')[0].split(' ')[0].split('|')[0].lower() + '-' + '-'.join(line.split('-')[1:]) + '\n'
        else:
            out += line
    with open(os.path. join(download_path, 'stand_' + os.path.split(file)[-1]), 'w', encoding='UTF-8') as f:
        f.write(out[1:])
    return os.path. join(download_path, 'stand_' + os.path.split(file)[-1])


def get_list(data):
    out = []
    for line in data:
        line = line.split('\t')
        if line[1] not in out:
            out.append(line[1])
    return out


def part_data(li_1, size_1):
    size_list_1 = []
    size_index_1 = []
    if '/' in size_1:
        size_list_1.append([0, int(float(size_1.split('/')[0])*li_1)])
        size_index_1.append(size_1.split('/')[0])
        size_list_1.append([int(float(size_1.split('/')[0])*li_1), li_1])
        size_index_1.append(size_1.split('/')[-1])
    else:
        gap = int(round(li_1*float(size_1), 0))
        t = 0
        for i in range(int(1/float(size_1))-1):
            size_index_1.append(str(i))
            size_list_1.append([t, t + gap])
            t += gap
        size_list_1.append([t, li_1])
        size_index_1.append(str(i+1))
    return size_list_1, size_index_1


def split_data(file_1, size_1, data):
    if file_1 not in os.listdir(download_path):
        os.makedirs(os.path.join(download_path, file_1))
    list_1 = []
    for i in data:
        i = i.split('\t')
        if file_1 == i[1]:
            list_1.append(os.path.join(os.path.join(data_path, i[1]), i[2]))
    size_list_1, size_index_1 = part_data(len(list_1), size_1)
    out_path = []
    for i in range(len(size_list_1)):
        key1 = size_list_1[i]
        name = size_index_1[i]
        out1 = ''
        for path in list_1[key1[0]:key1[1]]:
            with open(path, 'r') as f:
                out1 += f.read() + '\n'
        with open(os.path.join(os.path.join(download_path, file_1), file_1 + '&' + name + '.fasta'), 'w') as f:
            f.write(out1)
        out_path.append(os.path.join(os.path.join(download_path, file_1), file_1 + '&' + name + '.fasta'))
    return '\n'.join(out_path)


def save_data(data, name=None):
    path = []
    for i in data:
        i = i.split('\t')
        path.append(os.path.join(os.path.join(data_path, i[1]), i[2]))
    out = ''
    for each in path:
        with open(each, 'r') as f:
            out += f.read() + '\n'
    if name == None:
        name = visual_timestamp() + '.fasta'
    with open(os.path.join(download_path, name), 'w') as f:
        f.write(out)
    return os.path.join(download_path, name)







